Knowledge-based Decision Support Research Articles

Towards a knowledge-based decision support system to foster the return to work of wheelchair users

Accidents at work may force workers to face abrupt changes in their daily life: one of the most impactful accident cases consists of the worker remaining in a wheelchair. Return To Work (RTW) of wheelchair users in their working age is still challenging, encompassing the expertise of clinical and rehabilitation personnel and social workers to match the workers’ residual capabilities with job requirements. This work describes a novel and prototypical knowledge-based Decision Support System (DSS) that matches workers’ residual capabilities with job requirements, thus helping vocational therapists and clinical personnel in the RTW decision-making process for WUs. The DSS leverages expert knowledge in the form of ontologies to represent the International Classification of Functioning, Disability, and Health (ICF) and the Occupational Information Network (O*NET). These taxonomies enable both workers’ health conditions and job requirements formalization, which are processed to assess the suitability of a job depending on a worker’s condition. Consequently, the DSS suggests a list of jobs a wheelchair user can still perform, exploiting his/her residual abilities at their best. The manuscript describes the theoretical approach and technological foundations of such DSS, illustrating its development, its output metric, and application. The developed solution was tested with real wheelchair users’ health conditions provided by the Italian National Institute for Insurance against Accidents at Work. The feasibility of an approach based on objective data was thus demonstrated, providing a novel point of view in the critical process of decision-making during RTW.

INTELLIGENCE ANALYTICAL SUBSYSTEM OF CORPORATE MANAGEMENT (ON THE EXAMPLE OF AN OIL & GAS COMPANY)

The changes that have taken place in the Ukrainian economy in recent years due to the consistent impact of global macroeconomic, epidemiological and military factors of the current deep crisis - actualized for enterprises and corporations a complex adaptive reorganization of their analytical subsystem of their management [1, 2].
 In addition, in order to be consistently effective in today's global business environment (liberalization of markets, their globalization, increased competition, decreasing consumer loyalty, constant variation in oil and gas prices, etc.), oil and gas companies must have an innovative data and knowledge environment that allows seamless and effective joint leveraging knowledge inside the corporation and across the value chain [3]. The application of knowledge-based decision support systems as an important tool of innovative management policy enables effective and sustainable management of a complex of integrated processes of an oil and gas production company (mitigating the impact of crisis factors) [4].
 The scientific and practical results presented in the article regarding the methodology and design of innovative analytical subsystem of the corporation management information systems should be taken into account when reengineering corporate information systems not only in the oil and gas industry in Ukraine [5-6]. 
 Moreover, the obtained results are relevant and applicable not only for local companies, but also for international corporations on emerging markets in the context of global and regional macroeconomic and edipemical crisis phenomena.

A Knowledge-based Decision Support System for recommending safe recipes to individuals with dysphagia

BackgroundDysphagia is a disorder that can be associated to several pathological conditions, including neuromuscular diseases, with significant impact on quality of life. Dysphagia often leads to malnutrition, as a consequence of the dietary changes made by patients or their caregivers, who may deliberately decide to reduce or avoid specific food consistencies (because they are not perceived as safe), and the lack of knowledge in how to process foods are critics. Such dietary changes often result in unbalanced nutrients intake, which can have significant consequences for frail patients. This paper presents the development of a prototypical novel ontology-based Decision Support System (DSS) to support neuromuscular patients with dysphagia (following a per-oral nutrition) and their caregivers in preparing nutritionally balanced and safe meals. MethodAfter reviewing scientific literature, we developed in collaboration with Ear-Nose-Throat (ENT) specialists, neurologists, and dieticians the DSS formalizes expert knowledge to suggest recipes that are considered safe according to patient's consistency limitations and dysphagia severity and also nutritionally well-balanced. ResultsThe prototype can be accessed via digital applications both by physicians to generate and verify the recommendations, and by the patients and their caregivers to follow the step-by-step procedures to autonomously prepare and process one or more recipe. The system is evaluated with 9 clinicians to assess the quality of the DSS's suggested recipes and its acceptance in clinical practice. ConclusionsPreliminary results suggest a global positive outcome for the recipes inferred by the DSS and a good usability of the system.

ANItA – A new Swedish national competence centre in new nuclear power technology

In 2021, a proposal was submitted to the Swedish Energy Agency, which meant that the three leading Swedish universities: Uppsala University, KTH and Chalmers as well as the Swedish and Finnish nuclear power industry would together form a national competence centre for research and development work regarding the introduction of small modular reactors in Sweden. The proposal, called, ANItA (Academic-industrial Nuclear technology Initiative to Achieve a sustainable energy future) entailed a commitment for ten years and was granted funds for the first five years with an option for continued funding for the following five years. The initial research portfolio includes fourteen projects spanning a wide range of technical and non-technical aspects of small modular reactor (SMR) technology. During the last few months, the projects have been staffed with both national and international doctoral students and postdocs and thus constitute a contribution to securing the important supply of expertise in the nuclear field in Sweden. ANItA has attracted great interest from the surrounding society, which includes both politics and mass media and discussions are being held with potential new industrial partners. The renewed interest in new nuclear power in Sweden has further strengthened ANItA's role and the development is moving in a direction that enables to fulfil the basic idea behind ANItA, namely that through research and development work produce knowledge-based decision support for politicians and policymakers in the introduction of new nuclear power technology in Sweden.

A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus.

Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time. In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients. Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated. After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions were done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients. The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease.

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

A knowledge-based decision support system for incident traffic congestion management

A Knowledge-based Decision Support System (KB-DSS) based on a multi-stage Fuzzy Logic Controller (MS-FLC) is developed for traffic congestion management on expressways. The MS-FLC receives real-time traffic and incident data to analyse and anticipate the traffic conditions, to recommend alternative control measures in the form of natural languages for the human operator to select control decisions, and to calculate control settings to manage traffic congestion. In a case study, the KB-DSS is evaluated on a simulated network in comparison to ALINEA\Q, a popular ramp control method, across various traffic and incident situations. The results showed that: (i) the KB-DSS provides a systematic procedure in deriving control actions and a good capability to deliver linguistic expressions; (ii) the KB-DSS outperforms ALINEA\Q with respect to global objectives across many scenarios, attains significant improvements of mainline travel conditions and substantial reductions of ramp queues. These advantages make the KB-DSS a robust tool for traffic control for incident congestion management on expressways.

Conditional Knowledge Extraction Using Contextual Information Enhancement

Conditional phrases provide fine-grained domain knowledge in various industries, including medicine, manufacturing, and others. Most existing knowledge extraction research focuses on mining triplets with entities and relations and treats that triplet knowledge as plain facts without considering the conditional modality of such facts. We argue that such approaches are insufficient in building knowledge-based decision support systems in vertical domains, where specific and professional instructions on what facts apply under given circumstances are indispensable. To address this issue, this paper proposes a condition-aware knowledge extraction method using contextual information. In particular, this paper first fine-tunes the pre-training model to leverage a local context enhancement to capture the positional context of conditional phrases; then, a sentence-level context enhancement is used to integrate sentence semantics; finally, the correspondences between conditional phrases and relation triplets are extracted using syntactic attention. Experimental results on public and proprietary datasets show that our model can successfully retrieve conditional phrases with relevant triplets while improving the accuracy of the matching task by 2.68%, compared to the baseline.

Combining the External Medical Knowledge Graph Embedding to Improve the Performance of Syndrome Differentiation Model.

The electronic medical records (EMRs) of traditional Chinese medicine (TCM) include a wealth of TCM knowledge and syndrome diagnosis information, which is crucial for improving the quality of TCM auxiliary decision-making. In practical diagnosis, one disease corresponds to one syndrome, posing considerable hurdles for the informatization of TCM. The purpose of this work was to create an end-to-end TCM diagnostic model, and the knowledge graph (KG) created in this article is used to improve the model's information and realize auxiliary decision-making for TCM disorders. We approached auxiliary decision-making for syndrome differentiation in this article as a multilabel classification task and presented a knowledge-based decision support model for syndrome differentiation (KDSD). Specifically, we created a KG based on TCM features (TCMKG), supplementing the textual representation of medical data with embedded information. Finally, we proposed fusing medical text with KG entity representation (F-MT-KER) to get prediction results using a linear output layer. After obtaining the vector representation of the medical record text using the BERT model, the vector representation of various KG embedded models can provide additional hidden information to a certain extent. Experimental results show that our method improves by 1% (P@1) on the syndrome differentiation auxiliary decision task compared to the baseline model BERT. The usage of EMRs can aid TCM development more efficiently. With the help of entity level representation, character level representation, and model fusion, the multilabel classification method based on the pretraining model and KG can better simulate the TCM syndrome differentiation of the complex cases.

Ending the suitability quantification dilemma: intelligent decision support system for modular integrated construction in a high-density metropolis

Purpose Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method. Design/methodology/approach The KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS. Findings The tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem. Originality/value This study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.

A Lean Knowledge-Based Decision Support System for Cost Estimation in the Foundry Industry

A Lean Knowledge-Based Decision Support System for Cost Estimation in the Foundry Industry

Investigating economic replacement policy under uncertainty for managerial application based on grey-reliability approach using QCC

PurposeThe main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.Design/methodology/approachThe study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.FindingsThe findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.Originality/valueA conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.

Integrated framework of knowledge-based decision support system for user-centered residential design

A user-oriented residential environment is considered essential to providing occupants with a comfortable living experience while meeting their demands. However, design assessment in the early stage of residential design is a complex problem due to multifactor coupling, the diversity in user needs, and the incompleteness of design knowledge. This study proposes an integrated framework of knowledge-based decision support system (KBDSS) to mitigate such problems. In this framework, the user segmentation is performed in accordance with people’s particular requirements priority and, thus, enables design decisions that create better satisfaction among a wider audience. Meanwhile, the quality function deployment approach, decision support system, and fuzzy set theory are used to translate user requirements into quantifiable design specifications, thereby making the design a multicriteria decision-making problem. To illustrate and validate the proposed framework, a case study of a KBDSS for a multiunit residential building kitchen design is demonstrated to explore a user-centered residential design with such decision support. This proposed framework provides naive design practitioners with specific knowledge about decision problems and assists them in consistently making user-centered design decisions. The research results represent a further step toward a systematic methodology of user-centered built environment design.

An interpretable knowledge-based decision support method for ship collision avoidance using AIS data

AIS data include ship spatial-temporal and motion parameters which can be used to excavate the deep-seated information. In this article, an interpretable knowledge-based decision support method is established to guide the ship to make collision avoidance decisions with good seamanship and ordinary practice of seamen using AIS data. First, AIS data is preprocessed and trajectory reconstructed to restore the ship historical navigation state, and a ship encounter identification model is constructed according to the encounter characteristics; Second, a two-stage collision avoidance behavior extraction algorithm is formed to build a behavior knowledge base, and the scenario similarity model is constructed to measure and match similar scenarios based on ship position, motion tendency and collision risk. Then, the Delaunay Triangulation Network is used to fuse ship trajectories of similar scenario to form the collision avoidance path. Finally, a case study is performed using the real AIS data outside Ningbo-Zhoushan Port waters, China, and the effectiveness of the planned path is verified by setting the head-on and crossing situations and comparison between the planned and real paths. Results indicate that the proposed model can extract the ship collision avoidance behavior accurately, and the planned path can ensure navigation safety.

К проблеме использования знаний в системах управления рисками

The article dwells on the concept of building intelligent, knowledge-based decision support systems. A formalized risk management system is proposed. Based on the system, the author puts forward a solution to a particular problem from the banking sector in implementing projects related to distressed assets. Description and requirements to the tool for development, implementation and use of expert systems are presented.

A KNOWLEDGE-BASED DECISION SUPPORT SYSTEM FOR SELECTION OF SUSTAINABLE STRUCTURALMATERIALS: USINGFUZZY EXPERT SYSTEM

One of the most critical elements in the construction industry is the materials used in the structure. The selection of appropriate materials can significantly impact cost, durability, beauty of the building, and many other aspects. Given the importance of sustainable development, it is necessary to carefully examine all the sustainability dimensions, including economic, social, environmental, and technical, which impact both the construction and operation phases of a building lifecycle. This study develops a Fuzzy Expert System (FES) as a knowledge-based decision support system (KBDSS) to rank structural materials according to sustainability criteria. A total of twelve sub-criteria are considered, three under each sustainability criterion. The FES is divided into five sub-systems to reduce the number of rules. A total of 243 rules are implemented in the model using MATLAB Fuzzy Logic Designer Toolbox. A case study was performed to evaluate four structural materials for sustainability criteria. These materials are: timber, concrete, steel, and blended cement. The results of the case study show that timber is the most sustainable material according to user input. However, the developed model can be easily calibrated to reflect changes in experts’ opinions, leading to a different ranking of structural materials.

Knowledge-based decision support for BIM adoption by small and medium-sized enterprises in developing economies

BIM is often considered to be unsuitable and there are no systematic approaches to guiding suitability decisions in construction SMEs, especially in developing economies. Thus, this paper evaluated key decision-making factors (DMFs) for BIM-based projects in SMEs. Data was collected through interviews, a Delphi survey and analysed with Fuzzy Synthetic Evaluation (FSE). The result revealed that contractual factors, client requirements and project features are the most important DMF categories. Suitability Decision Support Index (SDSI) was computed, and action plans were compiled. These serve as a decision support engine and a knowledge base in developing a Knowledge-Based Decision Support System (KBDSS). The KBDSS validation showed that it is a useful tool for providing reliable decision support for SMEs. The findings provide solid empirical support for the evaluation of BIM in SMEs from a decision-making perspective. It has significant implications for policy and research and provides ground for technology suitability theory.

A KNOWLEDGE-BASED DECISION SUPPORT SYSTEM FOR SELECTION OF SUSTAINABLE STRUCTURALMATERIALS: USINGFUZZY EXPERT SYSTEM

One of the most critical elements in the construction industry is the materials used in the structure. The selection of appropriate materials can significantly impact cost, durability, beauty of the building, and many other aspects. Given the importance of sustainable development, it is necessary to carefully examine all the sustainability dimensions, including economic, social, environmental, and technical, which impact both the construction and operation phases of a building lifecycle. This study develops a Fuzzy Expert System (FES) as a knowledge-based decision support system (KBDSS) to rank structural materials according to sustainability criteria. A total of twelve sub-criteria are considered, three under each sustainability criterion. The FES is divided into five sub-systems to reduce the number of rules. A total of 243 rules are implemented in the model using MATLAB Fuzzy Logic Designer Toolbox. A case study was performed to evaluate four structural materials for sustainability criteria. These materials are: timber, concrete, steel, and blended cement. The results of the case study show that timber is the most sustainable material according to user input. However, the developed model can be easily calibrated to reflect changes in experts’ opinions, leading to a different ranking of structural materials.

Digitalization Platform for Mechanistic Modeling of Battery Cell Production

The application of batteries in electric vehicles and stationary energy-storage systems is widely seen as a promising enabler for a sustainable mobility and for the energy sector. Although significant improvements have been achieved in the last decade in terms of higher battery performance and lower production costs, there remains high potential to be tapped, especially along the battery production chain. However, the battery production process is highly complex due to numerous process–structure and structure–performance relationships along the process chain, many of which are not yet fully understood. In order to move away from expensive trial-and-error operations of production lines, a methodology is needed to provide knowledge-based decision support to improve the quality and throughput of battery production. In the present work, a framework is presented that combines a process chain model and a battery cell model to quantitatively predict the impact of processes on the final battery cell performance. The framework enables coupling of diverse mechanistic models for the individual processes and the battery cell in a generic container platform, ultimately providing a digital representation of a battery electrode and cell production line that allows optimal production settings to be identified in silico. The framework can be implemented as part of a cyber-physical production system to provide decision support and ultimately control of the production line, thus increasing the efficiency of the entire battery cell production process.

Temporal reasoning and query answering with preferences and probabilities for medical decision support

Knowledge-based decision support systems have a long tradition within the medical area. In particular, in the last decades, many Computer-Interpretable Guidelines (CIG) systems have been built to provide evidence-based and knowledge-based support to physicians. Since CIGs are, by definition, devoted to the management of specific diseases, the treatment of comorbid patients constitutes a challenging task in the area, involving (i) the detection of the possible interactions between (the effects of) the actions recommended by multiple CIGs (one for each disease of the patient), (ii) the management of such interactions and, finally, (iii) the conciliation of (the recommendations of) different CIGs. This paper focuses on issue (i) above, and specifically, on an innovative approach to support interaction detection along the temporal dimension. Practically, interactions can only occur between effects that intersect in time. Therefore, interaction detection involves the representation of temporal information (temporal constraints), and temporal reasoning (to propagate such constraints). Additionally, query answering facilities are important to support physicians in the investigation of the results of temporal reasoning. Current CIG approaches that face such issues take into account only “crisp” temporal constraints, i.e., they consider all temporal constraints as equally probable\\preferred. However, preferences about the temporal constraints between CIGs actions may be available, as well as knowledge about the probabilistic distribution of the effects of CIGs actions in time. Considering such additional pieces of information can provide crucial advantages, in term of the flexibility and informativeness of the support provided by the CIG system to physicians. In this paper, we propose the first homogeneous approach to represent and reason with (propagate) temporal constraints with both preferences and probabilities. We ground our approach on the widely-used Simple Temporal Problem (STP) framework, which supports temporal reasoning on temporal constraints about possible distances between events. We extend (i) the representation formalism to associate preferences and\\or probabilities to the possible distances, and (ii) the operations to propagate the constraints to combine also preferences and probabilities. We also (iii) provide an experimental evaluation of our approach, and (iv) propose a wide range of query-answering supports, to facilitate physicians in the analysis of the results of temporal reasoning in general, and in the temporal detection of possible interactions in particular. Finally, (v) we also show how such a temporal framework is integrated in GLARE-SSCPM, a CIG system to treat comorbid patients, and show the advantages of our approach considering a running example.